🥳🥳Launch week sale🥳🥳75% off all exams for a limited time celebrating our launch!!
75% off$39 $9.75Shop the sale
Amazon Web Services

AWS Certified Machine Learning Engineer - Associate

Associate AWS certification for data preparation, model development, workflow deployment, monitoring, maintenance, security, and responsible AI.

Practice

Learn at your own pace. Answer questions one at a time with instant feedback and explanations.

Start practice

Mock exam

Simulate the real thing. Take a timed, full-length test and review your score and weak areas.

Sign up to start
Get full access Unlimited practice and timed mock exams for 90 days. Create your account at checkout.
$39 You save $29.25 today

Study your way: beyond Practice and Mock exam, choose adaptive, hard mode, ready review, objective coverage, or retry-your-misses — and set your own question count, timer, and pass mark.

About this exam

AWS Certified Machine Learning Engineer - Associate validates practical ML engineering skills across data preparation, model building, deployment, workflow orchestration, monitoring, security, and governance on AWS.

Who should take this exam

ML engineers, data scientists, data engineers, and cloud practitioners who build, deploy, operate, and secure ML workloads on AWS.

Career benefits

Demonstrates applied AWS ML engineering ability for production model pipelines, inference workloads, responsible AI, security, and operational excellence.

How to prepare

Use the official exam guide, SageMaker and AWS AI/ML documentation, and hands-on work with feature engineering, training, evaluation, deployment, pipelines, monitoring, and security.

Quick facts

Exam cost$150 USD
Valid for3 years
Length130 minutes
Questions on exam65
Passing score720 / 1000
FormatMultiple choice and multiple response
Practice questions125
Objectives4
Official pageView

What's covered

1. Data preparation for machine learning

28%
  • 1.1 Ingest and store data for machine learning
  • 1.2 Transform data and perform feature engineering
  • 1.3 Validate data quality and prepare datasets

2. ML model development

26%
  • 2.1 Choose a modeling approach and algorithm
  • 2.2 Train, tune, and evaluate models
  • 2.3 Use AWS services for model building

3. Deployment and orchestration of ML workflows

22%
  • 3.1 Deploy models for inference
  • 3.2 Automate ML workflows and pipelines
  • 3.3 Monitor and maintain deployed models

4. ML solution monitoring, maintenance, and security

24%
  • 4.1 Apply security controls to ML solutions
  • 4.2 Implement responsible AI and governance controls
  • 4.3 Optimize ML solutions for reliability, performance, and cost

Frequently asked questions

Are these real exam questions?

No. These are original practice questions written to match the exam objectives, each with an explanation so you actually learn the material — not exam dumps.

How does practice mode work?

You answer questions one at a time with instant feedback and explanations. Over time the app adapts, prioritizing the objectives and questions you struggle with most.

What is a mock exam?

A timed, full-length simulation that holds feedback until the end, then shows your score, pass/fail result, and a breakdown by objective.

Can I customize how I study?

Yes. Pick the study mode that fits — adaptive practice, hard mode, ready-for-review, objective coverage, or retrying questions you've missed — and set your own question count, timer, and passing score for each session.

Do I need an account?

You can try free questions for this exam without signing in. Create a free account to save your progress, track weak objectives, and unlock the full question bank.

Study resources

An unhandled error has occurred. Reload 🗙

Rejoining the server...

Rejoin failed... trying again in seconds.

Failed to rejoin.
Please retry or reload the page.

The session has been paused by the server.

Failed to resume the session.
Please retry or reload the page.